Power Converter Design With Coupled Thermal Analysis Using Multidisciplinary Design Optimization

1994 ◽  
Author(s):  
Gregory Kott ◽  
Gary A. Gabriele ◽  
Jacob Korngold
Keyword(s):  
Thermal Analysis ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Power Converter ◽  
Multidisciplinary Design ◽  
Total System ◽  
Global Approximation ◽  
Stage Design ◽  
Power Stage ◽  
Percent Decrease

Abstract This paper describes the application of multidisciplinary design optimization to the power stage design of a power converter. Multidisciplinary design is used to integrate the electrical, loss, and thermal analyses into one system problem. The Sequential Global Approximation method, a non-hierarchic algorithm, is used to optimize the power stage design problem. The code used for the thermal analysis, COSMOS/M, runs externally to the Sequential Global Approximation algorithm. A comparison of the results of the non-hierarchic formulation and the non-decomposed formulation shows a 67 percent decrease in total system iterations and a 12 percent decrease in total finite element analyses required.

Application of Multidisciplinary Design Optimization to the Power Stage Design of a Power Converter

1993 ◽  
Author(s):  
Gregory Kott ◽  
Gary A. Gabriele ◽  
Jacob Korngold
Keyword(s):  
Design Optimization ◽  
Design Problem ◽  
Multidisciplinary Design Optimization ◽  
Power Converter ◽  
Multidisciplinary Design ◽  
Successful Implementation ◽  
Total System ◽  
Global Approximation ◽  
Stage Design ◽  
Power Stage

Abstract This paper describes the application of multidisciplinary design optimization to the power stage design of a power converter. The decomposition of the power stage design into an electrical and a loss subsystem is developed. The Sequential Global Approximation method is the non-hierarchic algorithm used to optimize the power stage design problem. Results of the non-hierarchic formulation compared to the non-decomposed formulation show a decrease of 63 percent in total system iterations required to converge to the optimal solution. Local and global move limits of 28 percent were found to provide the best performance for this problem. The successful implementation and results of applying multidisciplinary design optimization to power stage design allows the extension of the research to incorporate other disciplines. Our goal is to include all disciplines to completely model the design of a power converter. The details of power stage design problem formulation are provided to be used as a test problem in multidisciplinary design optimization research.

Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization

AIAA Journal ◽  
2001 ◽  
Vol 39 (12) ◽  
pp. 2233-2241 ◽  
Author(s):  
Timothy W. Simpson ◽  
Timothy M. Mauery ◽  
John J. Korte ◽  
Farrokh Mistree
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Multidisciplinary Design ◽  
Global Approximation ◽  
Simulation Based

Employing Knowledge on Causal Relationship to Assist Multidisciplinary Design Optimization

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Di Wu ◽  
Eric Coatanea ◽  
G. Gary Wang
Keyword(s):  
Qualitative Analysis ◽  
Design Optimization ◽  
Causal Relationship ◽  
Design Problem ◽  
Multidisciplinary Design Optimization ◽  
Power Converter ◽  
Multidisciplinary Design ◽  
Concept Design ◽  
Causal Graph ◽  
The Impact

With the increasing design dimensionality, it is more difficult to solve multidisciplinary design optimization (MDO) problems. Many MDO decomposition strategies have been developed to reduce the dimensionality. Those strategies consider the design problem as a black-box function. However, practitioners usually have certain knowledge of their problem. In this paper, a method leveraging causal graph and qualitative analysis is developed to reduce the dimensionality of the MDO problem by systematically modeling and incorporating the knowledge about the design problem into optimization. Causal graph is created to show the input–output relationships between variables. A qualitative analysis algorithm using design structure matrix (DSM) is developed to automatically find the variables whose values can be determined without resorting to optimization. According to the impact of variables, an MDO problem is divided into two subproblems, the optimization problem with respect to the most important variables, and the other with variables of lower importance. The novel method is used to solve a power converter design problem and an aircraft concept design problem, and the results show that by incorporating knowledge in form of causal relationship, the optimization efficiency is significantly improved.

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

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